The Question :
339 people think this question is useful
I can’t figure out how to use an array or matrix in the way that I would normally use a list. I want to create an empty array (or matrix) and then add one column (or row) to it at a time.
At the moment the only way I can find to do this is like:
mat = None
for col in columns:
if mat is None:
mat = col
else:
mat = hstack((mat, col))
Whereas if it were a list, I’d do something like this:
list = []
for item in data:
list.append(item)
Is there a way to use that kind of notation for NumPy arrays or matrices?
The Question Comments :
The Answer 1
476 people think this answer is useful
You have the wrong mental model for using NumPy efficiently. NumPy arrays are stored in contiguous blocks of memory. If you want to add rows or columns to an existing array, the entire array needs to be copied to a new block of memory, creating gaps for the new elements to be stored. This is very inefficient if done repeatedly to build an array.
In the case of adding rows, your best bet is to create an array that is as big as your data set will eventually be, and then assign data to it rowbyrow:
>>> import numpy
>>> a = numpy.zeros(shape=(5,2))
>>> a
array([[ 0., 0.],
[ 0., 0.],
[ 0., 0.],
[ 0., 0.],
[ 0., 0.]])
>>> a[0] = [1,2]
>>> a[1] = [2,3]
>>> a
array([[ 1., 2.],
[ 2., 3.],
[ 0., 0.],
[ 0., 0.],
[ 0., 0.]])
The Answer 2
105 people think this answer is useful
A NumPy array is a very different data structure from a list and is designed to be used in different ways. Your use of hstack
is potentially very inefficient… every time you call it, all the data in the existing array is copied into a new one. (The append
function will have the same issue.) If you want to build up your matrix one column at a time, you might be best off to keep it in a list until it is finished, and only then convert it into an array.
e.g.
mylist = []
for item in data:
mylist.append(item)
mat = numpy.array(mylist)
item
can be a list, an array or any iterable, as long
as each item
has the same number of elements.
In this particular case (data
is some iterable holding the matrix columns) you can simply use
mat = numpy.array(data)
(Also note that using list
as a variable name is probably not good practice since it masks the builtin type by that name, which can lead to bugs.)
EDIT:
If for some reason you really do want to create an empty array, you can just use numpy.array([])
, but this is rarely useful!
The Answer 3
66 people think this answer is useful
To create an empty multidimensional array in NumPy (e.g. a 2D array m*n
to store your matrix), in case you don’t know m
how many rows you will append and don’t care about the computational cost Stephen Simmons mentioned (namely rebuildinging the array at each append), you can squeeze to 0 the dimension to which you want to append to: X = np.empty(shape=[0, n])
.
This way you can use for example (here m = 5
which we assume we didn’t know when creating the empty matrix, and n = 2
):
import numpy as np
n = 2
X = np.empty(shape=[0, n])
for i in range(5):
for j in range(2):
X = np.append(X, [[i, j]], axis=0)
print X
which will give you:
[[ 0. 0.]
[ 0. 1.]
[ 1. 0.]
[ 1. 1.]
[ 2. 0.]
[ 2. 1.]
[ 3. 0.]
[ 3. 1.]
[ 4. 0.]
[ 4. 1.]]
The Answer 4
26 people think this answer is useful
I looked into this a lot because I needed to use a numpy.array as a set in one of my school projects and I needed to be initialized empty… I didn’t found any relevant answer here on Stack Overflow, so I started doodling something.
# Initialize your variable as an empty list first
In [32]: x=[]
# and now cast it as a numpy ndarray
In [33]: x=np.array(x)
The result will be:
In [34]: x
Out[34]: array([], dtype=float64)
Therefore you can directly initialize an np array as follows:
In [36]: x= np.array([], dtype=np.float64)
I hope this helps.
The Answer 5
8 people think this answer is useful
You can use the append function. For rows:
>>> from numpy import *
>>> a = array([10,20,30])
>>> append(a, [[1,2,3]], axis=0)
array([[10, 20, 30],
[1, 2, 3]])
For columns:
>>> append(a, [[15],[15]], axis=1)
array([[10, 20, 30, 15],
[1, 2, 3, 15]])
EDIT
Of course, as mentioned in other answers, unless you’re doing some processing (ex. inversion) on the matrix/array EVERY time you append something to it, I would just create a list, append to it then convert it to an array.
The Answer 6
5 people think this answer is useful
Here is some workaround to make numpys look more like Lists
np_arr = np.array([])
np_arr = np.append(np_arr , 2)
np_arr = np.append(np_arr , 24)
print(np_arr)
OUTPUT: array([ 2., 24.])
The Answer 7
3 people think this answer is useful
If you absolutely don’t know the final size of the array, you can increment the size of the array like this:
my_arr = numpy.zeros((0,5))
for i in range(3):
my_arr=numpy.concatenate( ( my_arr, numpy.ones((1,5)) ) )
print(my_arr)
[[ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.]]
 Notice the
0
in the first line.
numpy.append
is another option. It calls numpy.concatenate
.
The Answer 8
3 people think this answer is useful
You can apply it to build any kind of array, like zeros:
a = range(5)
a = [i*0 for i in a]
print a
[0, 0, 0, 0, 0]
The Answer 9
2 people think this answer is useful
Depending on what you are using this for, you may need to specify the data type (see ‘dtype’).
For example, to create a 2D array of 8bit values (suitable for use as a monochrome image):
myarray = numpy.empty(shape=(H,W),dtype='u1')
For an RGB image, include the number of color channels in the shape: shape=(H,W,3)
You may also want to consider zeroinitializing with numpy.zeros
instead of using numpy.empty
. See the note here.
The Answer 10
1 people think this answer is useful
I think you want to handle most of the work with lists then use the result as a matrix. Maybe this is a way ;
ur_list = []
for col in columns:
ur_list.append(list(col))
mat = np.matrix(ur_list)
The Answer 11
1 people think this answer is useful
I think you can create empty numpy array like:
>>> import numpy as np
>>> empty_array= np.zeros(0)
>>> empty_array
array([], dtype=float64)
>>> empty_array.shape
(0,)
This format is useful when you want to append numpy array in the loop.
The Answer 12
1 people think this answer is useful
For creating an empty NumPy array without defining its shape:

arr = np.array([])
(this is preferred, because you know you will be using this as a NumPy array)

arr = [] # and use it as NumPy array later by converting it
arr = np.asarray(arr)
NumPy converts this to np.ndarray type afterward, without extra []
‘dimension’.
The Answer 13
0 people think this answer is useful
Perhaps what you are looking for is something like this:
x=np.array(0)
In this way you can create an array without any element. It similar than:
x=[]
This way you will be able to append new elements to your array in advance.